Satellite measurements match model results apart from in the tropics. There is uncertainty with the tropic data due to how various teams correct for satellite drift. The U.S. Climate Change Science Program conclude the discrepancy is most likely due to data errors.

Who’s desperate to find the missing hot-spot? Sherwood’s new paper claims to have found it, but after years of multi-layered adjustments, and now kriging the gaps, and iteratively homogenizing, the results of the new data partly “solve” one problem while creating others. There’s no documented, physical reason for the homogenizing and there’s no new insight gained. The raw data was used by airlines, the military, and meteorologists for years, yet the suggested new results are quite different to the raw data. It’s as if we can’t even measure air temperature properly. Somehow we’ve made multivariate complex models work but not simple temperature sensors? The main problem with the old results was that they didn’t fit the models. Now, after torturing the data, they still don’t.

Twenty-eight million weather balloons had shown by 1999 that the key assumption in the climate models was wrong. Without feedbacks, the models only produce 1.2°C of warming with a doubling of CO2. With feedbacks the simulations ramp that up to a dangerous 3 – 4 degrees C, and water vapor was the most important feedback. It’s just no fun for the Global Worriers without it.

I’ve looked through the paper and find the statistical black box approach they used to be unconvincing. I’ll leave it to others to examine the details of their statistical adjustments, what what the physical reasons for those adjustments might be.

These analysis results would appear to leave very, very little doubt but that EPA’s claim of a Tropical Hot Spot (THS), caused by rising atmospheric CO2 levels, simply does not exist in the real world. Also critically important, even on an all-other-things-equal basis, this analysis failed to find that the steadily rising Atmospheric CO2 Concentrations have had a statistically significant impact on any of the 13 critically important temperature time series analyzed. Thus, the analysis results invalidate each of the Three Lines of Evidence in its CO2 Endangerment Finding. Once EPA’s THS assumption is invalidated, it is obvious why the climate models they claim can be relied upon, are also invalid. And, these results clearly demonstrate–13 times in fact–that once just the ENSO impacts on temperature data are accounted for, there is no“record setting” warming to be concerned about. In fact, there is no ENSO-Adjusted Warming at all. These natural ENSO impacts involve both changes in solar activity and the 1977 Pacific Shift. Moreover, on an all-other-things-equal basis, there is no statistically valid proof that past increases in Atmospheric CO2 Concentrations have caused the officially reported rising, even claimed record setting temperatures. To validate their claim will require mathematically credible, publically available, simultaneous equation parameter estimation work. The temperature data measurements that were analyzed were taken by many different entities using balloons, satellites, buoys and various land based techniques. Needless to say, if regardless of data source, the results are the same, the analysis findings should be considered highly credible.

Plausible reasons for the inconsistencies between the modeled and observed temperatures in the tropical troposphereWe hereby attempt to detect plausible reasons for the discrepancies between the measured and modeled tropospheric temperature anomalies in the tropics. For this purpose, we calculate the trends of the upper-minus-lower tropospheric temperature anomaly differences (TAD) for both the measured and modeled time series during 1979–2010. The modeled TAD trend is significantly higher than that of the measured ones, confirming that the vertical amplification of warming is exaggerated in models. To investigate the cause of this exaggeration, we compare the intrinsic properties of the measured and modeled TAD by employing detrended fluctuation analysis (DFA). The DFA exponent obtained for the measured values reveals white noise behavior, while the exponent for the modeled ones shows that they exhibit long-range power law correlations. We suggest that the vertical amplification of warming derived from modeled simulations is weighted with a persistent signal, which should be removed in order to achieve better agreement with observations.